Structural Dynamics and Allosteric Signaling in Ionotropic Glutamate Receptors

Ionotropic glutamate receptors (iGluRs) are ligand-gated ion channels that mediate excitatory neurotransmission events in the central nervous system. All distinct classes of iGluRs (AMPA, NMDA, Kainate) are composed of an N-terminal domain (NTD) and a ligand-binding domain (LBD) in their extracellular domain, a transmembrane domain (TMD) and an intracellular carboxy-terminal domain (CTD). Ligand binding to the LBD facilitates ion channel activation. The NTDs modulate channel gating allosterically in NMDA receptors (NMDARs). A similar function of the NTD in AMPA receptors (AMPARs) is still a matter of debate. Taking advantage of recently resolved structures of the NTD and the intact AMPAR, the main focus of this dissertation is a comprehensive examination of iGluR NTD structural dynamics, ligand binding and allosteric potential of AMPARs. We use a multiscale, multi-dimensional approach using coarse-grained network models and all-atom simulations for structural analyses and information theoretic approaches for examination of evolutionary correlations. Our major contribution has been the characterization of the global motions favored by iGluR NTD architecture. These intrinsic motions favor ligand binding in NMDAR NTDs and are also shared by other iGluR NTDs. We also identified structural determinants of flexibility in AMPARs and confirmed their role through in silico mutants. The overall similarity in collective dynamics among iGluRs hints at a putative allosteric capacity of non-NMDARs and has propelled the elucidation of interdomain and intersubunit coupling in the intact AMPAR. To this end, we identified “effector” and “sensor” regions in AMPARs using a perturbation-response technique. We identified potentially functional residues that enable information propagation between effector regions and proposed an efficient mechanism of allosteric communication based on a combination of tools including network models, graph theoretical methods and sequence analyses. Finally, we assessed the “druggability” of iGluR NTDs using molecular dynamics simulations in the presence of probe molecules containing fragments shared by drug-like molecules. Based on our study, we offer key insights into the ligand-binding landscape of iGluR NTD monomers and dimers, and we also identify a novel ligand-binding site in AMPAR dimers. These findings open an avenue of searching for molecules able to bind to iGluR NTDs and allosterically modulate receptor activity

AlloPred: prediction of allosteric pockets on proteins using normal mode perturbation analysis

BACKGROUND: Despite being hugely important in biological processes, allostery is poorly understood and no universal mechanism has been discovered. Allosteric drugs are a largely unexplored prospect with many potential advantages over orthosteric drugs. Computational methods to predict allosteric sites on proteins are needed to aid the discovery of allosteric drugs, as well as to advance our fundamental understanding of allostery. RESULTS: AlloPred, a novel method to predict allosteric pockets on proteins, was developed. AlloPred uses perturbation of normal modes alongside pocket descriptors in a machine learning approach that ranks the pockets on a protein. AlloPred ranked an allosteric pocket top for 23 out of 40 known allosteric proteins, showing comparable and complementary performance to two existing methods. In 28 of 40 cases an allosteric pocket was ranked first or second. The AlloPred web server, freely available at http://www.sbg.bio.ic.ac.uk/allopred/home, allows visualisation and analysis of predictions. The source code and dataset information are also available from this site. CONCLUSIONS: Perturbation of normal modes can enhance our ability to predict allosteric sites on proteins. Computational methods such as AlloPred assist drug discovery efforts by suggesting sites on proteins for further experimental study